Jonathan Chassot
Last Name
Chassot
First name
Jonathan
Email
jonathan.chassot@unisg.ch
Phone
+41 71 224 27 97
4 results
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Item type:Publication, HARd to beat: The overlooked impact of rolling windows in the era of machine learning(2026-02); Type:journal articleJournal:International Journal of ForecastingVolume:42Issue:2Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine LearningWe investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1,445 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model’s performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting.Type:journal articleJournal:International Journal of ForecastingVolume:Forthcoming - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How does post-earnings announcements sentiment affect firms' dynamics? New evidence from causal machine learning(Oxford University Press, 2022-07-12); ; Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning(2020-12-22); ; ; ; We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end we apply causal machine learning on the earnings announcements of a wide cross-section of US companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher.Type:working paper